Title:Comprehensive Factors for Predicting the Complications of Diabetes
Mellitus: A Systematic Review
Volume: 20
Issue: 9
Author(s): Madurapperumage Anuradha Erandathi, William Yu Chung Wang*, Michael Mayo and Ching-Chi Lee
Affiliation:
- University of Waikato, Hamilton, New Zealand
Keywords:
Diabetes mellitus, risk factors, machine learning, complications of diabetes, cholesterol, triglyceride, BMI.
Abstract:
Background: This article focuses on extracting a standard feature set for predicting the
complications of diabetes mellitus by systematically reviewing the literature. It is conducted and
reported by following the guidelines of PRISMA, a well-known systematic review and meta-analysis
method. The research articles included in this study are extracted using the search engine
"Web of Science" over eight years. The most common complications of diabetes, diabetic neuropathy,
retinopathy, nephropathy, and cardiovascular diseases are considered in the study.
Method: The features used to predict the complications are identified and categorised by scrutinising
the standards of electronic health records.
Result: Overall, 102 research articles have been reviewed, resulting in 59 frequent features being
identified. Nineteen attributes are recognised as a standard in all four considered complications,
which are age, gender, ethnicity, weight, height, BMI, smoking history, HbA1c, SBP, eGFR, DBP,
HDL, LDL, total cholesterol, triglyceride, use of insulin, duration of diabetes, family history of
CVD, and diabetes. The existence of a well-accepted and updated feature set for health analytics
models to predict the complications of diabetes mellitus is a vital and contemporary requirement.
A widely accepted feature set is beneficial for benchmarking the risk factors of complications of
diabetes.
Conclusion: This study is a thorough literature review to provide a clear state of the art for academicians,
clinicians, and other stakeholders regarding the risk factors and their importance.
